Book Image

Hands-On Intelligent Agents with OpenAI Gym

By : Palanisamy P
Book Image

Hands-On Intelligent Agents with OpenAI Gym

By: Palanisamy P

Overview of this book

Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
Table of Contents (12 chapters)

Exploring the list of environments and nomenclature

Let's start by picking an environment and understanding the Gym interface. You may already be familiar with the basic function calls to create a Gym environment from the previous chapters, where we used them to test our installations. Here, we will formally go through them.

Let's activate the rl_gym_book conda environment and open a Python prompt. The first step is to import the Gym Python module using the following line of code:

import gym

We can now use the gym.make method to create an environment from the available list of environments. You may be asking how to find the list of Gym environments available on your system. We will create a small utility script to generate the list of environments so that you can refer to it later when you need to. Let's create a script named under the ~/rl_gym_book...